{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sales = pd.read_csv('sku_sales.csv')\n",
    "sales.columns = ['sku_id','dc_id','date','quantity','vend','or_price','discount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sku_id  dc_id        date  quantity  vend  or_price  discount\n",
      "0     637      0  2016-10-12       5.0   1.0  0.089886  7.858136\n",
      "1     637      0  2017-09-24       1.0   1.0  0.089886  8.191933\n",
      "2     637      3  2016-07-01       0.0   1.0       NaN       NaN\n",
      "3     637      3  2016-08-03       2.0   1.0  0.089886  8.122392\n",
      "4     637      3  2017-05-03       4.0   1.0  0.089886  8.136300\n",
      "Empty DataFrame\n",
      "Columns: [dc_id, sku_id, count, date, q]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "#date属性分析\n",
    "print sales.head()\n",
    "have_days=sales.sort_values(by=[ 'dc_id','sku_id','date'])\n",
    "have_days['count']=pd.Series(1, index=have_days.index)\n",
    "have_days=have_days.groupby(['dc_id','sku_id'])['count'].sum()\n",
    "\n",
    "from datetime import date\n",
    "from datetime import datetime\n",
    "def period(time):\n",
    "    time1=datetime.strptime(time.max(),\"%Y-%m-%d\")\n",
    "    time2=datetime.strptime(time.min(),\"%Y-%m-%d\")\n",
    "    return (time1-time2).days+1\n",
    "have=sales.sort_values(by=[ 'dc_id','sku_id','date'])\n",
    "have=have.groupby(['dc_id','sku_id'])['date'].agg(period)\n",
    "a=pd.merge(have_days.reset_index(),have.reset_index(),how='outer',on=['dc_id','sku_id'])\n",
    "a['q']=a['date']-a['count']\n",
    "print a[a['q']%30!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sku_id  first  second  third  brand\n",
      "999    1000      1       7    179    137\n"
     ]
    }
   ],
   "source": [
    "info=pd.read_csv('sku_info.csv')\n",
    "info = pd.DataFrame(info.values, columns=['sku_id','first','second','third','brand'])\n",
    "#print info.sort_values(by=['first','second','third','brand','sku_id'])\n",
    "#print info.sort_values(by=['brand','third'])\n",
    "aa=info.groupby('third')['sku_id'].unique().reset_index()\n",
    "aa['type']=aa['sku_id'].map(lambda x:len(x))\n",
    "print info[info.sku_id==1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attr\n",
      "1      [1525, 2617, 2618, 4326, 2615, 523, 1433, 3480...\n",
      "2                                       [148, 2720, 693]\n",
      "3      [4689, 4193, 4752, 4756, 4109, 991, 4363, 4755...\n",
      "5                                       [878, 879, 2904]\n",
      "6                             [81, 29, 3955, 3954, 2835]\n",
      "7                                               [33, 34]\n",
      "11                     [2345, 119, 58, 2344, 2343, 2913]\n",
      "14                                                [4082]\n",
      "15                                                [4059]\n",
      "16                                            [879, 880]\n",
      "17                                              [98, 97]\n",
      "18                                      [981, 982, 1263]\n",
      "19                                                [2525]\n",
      "20                              [2119, 4629, 2117, 2118]\n",
      "21                                            [737, 736]\n",
      "22     [4177, 3461, 156, 3892, 3894, 174, 1263, 2724,...\n",
      "24                                                [3861]\n",
      "25                                                [1559]\n",
      "26                                              [36, 35]\n",
      "29                                                [1115]\n",
      "30                                                  [86]\n",
      "34                                                 [981]\n",
      "37     [32, 299, 13, 90, 37, 236, 3474, 2115, 2749, 2...\n",
      "38     [4777, 4331, 4776, 2135, 4774, 4052, 4775, 404...\n",
      "39                                                [4559]\n",
      "41                                                [4558]\n",
      "42                                                  [20]\n",
      "43                                           [339, 2621]\n",
      "50     [950, 969, 2066, 4, 2065, 1250, 3945, 1071, 21...\n",
      "55     [4346, 4772, 4771, 4054, 4327, 4056, 4328, 477...\n",
      "                             ...                        \n",
      "751                       [3386, 2125, 4039, 3469, 3957]\n",
      "752                    [4033, 3861, 4, 3953, 4516, 4032]\n",
      "757                                                 [26]\n",
      "758                                         [3977, 3976]\n",
      "762                                               [4009]\n",
      "765                             [1435, 1431, 1540, 4690]\n",
      "770                                   [1263, 4090, 4089]\n",
      "845                                               [4572]\n",
      "868                                     [208, 2759, 209]\n",
      "869                                               [4560]\n",
      "872                                         [44, 43, 69]\n",
      "873                                         [4445, 1383]\n",
      "874                                               [4569]\n",
      "875                                               [4570]\n",
      "876                                                  [4]\n",
      "877                                               [4582]\n",
      "878                                               [4583]\n",
      "879                                         [4585, 4584]\n",
      "880                                               [4586]\n",
      "881                                               [1447]\n",
      "882                                               [1016]\n",
      "884                    [2420, 4, 2417, 2449, 2418, 2419]\n",
      "885                                 [134, 2265, 4, 2273]\n",
      "888                                               [2085]\n",
      "889                                               [1263]\n",
      "890                                               [4627]\n",
      "909                                   [1263, 1990, 1298]\n",
      "924                                             [71, 12]\n",
      "925                                               [4781]\n",
      "952                                         [4932, 4931]\n",
      "Name: attr_values, Length: 237, dtype: object\n"
     ]
    }
   ],
   "source": [
    "attr=pd.read_csv('sku_attr.csv')\n",
    "attr = pd.DataFrame(attr.values, columns=['sku_id','attr','attr_values'])\n",
    "print attr.groupby('attr')['attr_values'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10L 6L 1L 4L]\n"
     ]
    }
   ],
   "source": [
    "prom=pd.read_csv('sku_prom.csv')\n",
    "prom = pd.DataFrame(prom.values, columns=['date','sku_id','third','prom_type'])\n",
    "print prom.prom_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qqqY2bNiwlKpLktRpS0rsSU7uG30ncKjH/G5ge5Jjk5wGbAbuAe4FNrce8MfQ62C3u6oK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\n3gS8kd59rv+c3gO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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb5734e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7b4eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x391a6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x265c2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7c4390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kG+ldYW5zm3YK8Cp617j+M3o3BLoiyb8Bfr6qvnXQsi4BXl29m2GQ5B8D7wJu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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7c4c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26086c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7b1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x213cdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x214c9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paint=sales.groupby(['date','dc_id'])['quantity'].sum().reset_index()\n",
    "import matplotlib.pyplot as plt  \n",
    "from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY,MonthLocator\n",
    "def pp(data,cc):\n",
    "    fig=plt.figure(figsize=(8,4))\n",
    "    x=[]\n",
    "    y=[]\n",
    "    qq=1\n",
    "    for i in data.date:\n",
    "        x.append(qq)\n",
    "        qq+=1\n",
    "        y.append(float(data[data.date==i].quantity))\n",
    "\n",
    "    plt.xlabel(\"quantity-date\")       \n",
    "    #print x,y\n",
    "    plt.bar(x,y,color=cc)  \n",
    "    \n",
    "    plt.show() \n",
    "pp(paint[(paint.dc_id==0)&(paint.date<='2016-12-31')],'red')\n",
    "pp(paint[(paint.dc_id==0)&(paint.date>'2016-12-31')],'red')\n",
    "\n",
    "pp(paint[(paint.dc_id==1)&(paint.date<='2016-12-31')],'blue')\n",
    "pp(paint[(paint.dc_id==1)&(paint.date>'2016-12-31')],'blue')\n",
    "\n",
    "pp(paint[(paint.dc_id==2)&(paint.date<='2016-12-31')],'green')\n",
    "pp(paint[(paint.dc_id==2)&(paint.date>'2016-12-31')],'green')\n",
    "\n",
    "pp(paint[(paint.dc_id==3)&(paint.date<='2016-12-31')],'purple')\n",
    "pp(paint[(paint.dc_id==3)&(paint.date>'2016-12-31')],'purple')\n",
    "\n",
    "paint1=sales.groupby('date')['quantity'].sum().reset_index()\n",
    "pp(paint1[(paint1.date<='2016-12-31')],'black')\n",
    "pp(paint1[(paint1.date>'2016-12-31')],'black')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [date, sku_id, third, prom_type]\n",
      "Index: []\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coding\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "test=pd.read_csv('sku_prom_testing_2018Jan.csv')\n",
    "test=pd.DataFrame(test.values,columns=['date','sku_id','third','prom_type'])\n",
    "print test.sort_values(by=['date','sku_id'])[test.sku_id==-999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         sku_id  dc_id        date  quantity  vend  or_price   discount\n",
      "0           637      0  2016-10-12       5.0   1.0  0.089886   7.858136\n",
      "1           637      0  2017-09-24       1.0   1.0  0.089886   8.191933\n",
      "2           637      3  2016-07-01       0.0   1.0       NaN        NaN\n",
      "3           637      3  2016-08-03       2.0   1.0  0.089886   8.122392\n",
      "4           637      3  2017-05-03       4.0   1.0  0.089886   8.136300\n",
      "5           937      1  2016-02-12       0.0   0.0       NaN        NaN\n",
      "6           937      1  2017-02-01       0.0   0.0       NaN        NaN\n",
      "7           937      1  2017-03-25       0.0   0.0       NaN        NaN\n",
      "8           937      1  2017-04-24       0.0   1.0       NaN        NaN\n",
      "9           937      4  2017-12-14       0.0   1.0       NaN        NaN\n",
      "10          937      5  2017-12-28       0.0   1.0       NaN        NaN\n",
      "11          937      0  2016-01-14       2.0   1.0  0.014877   5.000000\n",
      "12          937      2  2016-03-09       0.0   0.0       NaN        NaN\n",
      "13          937      2  2016-07-03       7.0   1.0  0.016002   5.714062\n",
      "14          937      2  2017-05-26       4.0   1.0  0.021240   4.973514\n",
      "15          937      2  2017-09-25       2.0   1.0  0.021240   5.000000\n",
      "16          937      2  2017-10-28       7.0   1.0  0.021240   4.412007\n",
      "17          937      2  2017-12-12      18.0   1.0  0.020061   5.294448\n",
      "18          937      3  2016-02-27       0.0   0.0       NaN        NaN\n",
      "19          937      3  2016-03-03       0.0   0.0       NaN        NaN\n",
      "20          937      3  2016-04-19       0.0   0.0       NaN        NaN\n",
      "21          937      3  2016-05-09       0.0   0.0       NaN        NaN\n",
      "22          937      3  2016-05-20       0.0   0.0       NaN        NaN\n",
      "23          937      3  2016-10-20       0.0   0.0       NaN        NaN\n",
      "24          937      3  2017-01-21       0.0   0.0       NaN        NaN\n",
      "25          937      3  2017-01-24       0.0   0.0       NaN        NaN\n",
      "26          937      3  2017-02-19       0.0   0.0       NaN        NaN\n",
      "27          937      3  2017-05-20       0.0   1.0       NaN        NaN\n",
      "28          177      0  2017-07-08       0.0   0.0       NaN        NaN\n",
      "29          177      0  2017-12-02       9.0   1.0  0.001000   8.750000\n",
      "...         ...    ...         ...       ...   ...       ...        ...\n",
      "2127455     611      0  2017-08-09       0.0   0.0       NaN        NaN\n",
      "2127456     611      1  2017-03-11       5.0   1.0  0.011239   9.666296\n",
      "2127457     611      1  2017-10-30       5.0   1.0  0.012489   7.997998\n",
      "2127458     611      2  2017-03-10       4.0   1.0  0.011239   9.860957\n",
      "2127459     611      4  2017-02-14       2.0   1.0  0.009989  10.000000\n",
      "2127460     611      4  2017-03-25       1.0   1.0  0.011239   9.443826\n",
      "2127461     611      4  2017-09-13       1.0   1.0  0.012489   8.998999\n",
      "2127462     611      3  2016-12-07       1.0   1.0  0.009114   9.588477\n",
      "2127463     611      3  2016-12-24       0.0   1.0       NaN        NaN\n",
      "2127464     873      5  2017-12-19       0.0   1.0       NaN        NaN\n",
      "2127465     873      2  2016-01-25       1.0   1.0  0.012377  10.000000\n",
      "2127466     873      2  2016-05-06       1.0   1.0  0.012377  10.000000\n",
      "2127467     873      2  2016-12-11       0.0   1.0       NaN        NaN\n",
      "2127468     873      2  2017-01-27       0.0   1.0       NaN        NaN\n",
      "2127469     873      2  2017-04-11       0.0   1.0       NaN        NaN\n",
      "2127470     873      4  2017-01-10       1.0   1.0  0.012377  10.000000\n",
      "2127471     873      4  2017-05-21       0.0   1.0       NaN        NaN\n",
      "2127472     873      4  2017-08-06       0.0   0.0       NaN        NaN\n",
      "2127473     873      1  2016-01-02       1.0   0.0  0.012377  10.000000\n",
      "2127474     873      1  2017-09-21       0.0   1.0       NaN        NaN\n",
      "2127475     873      3  2016-08-24       0.0   1.0       NaN        NaN\n",
      "2127476     873      3  2016-12-24       0.0   1.0       NaN        NaN\n",
      "2127477     873      3  2017-01-10       0.0   1.0       NaN        NaN\n",
      "2127478     873      3  2017-12-13       7.0   1.0  0.011126  10.000000\n",
      "2127479     873      0  2016-04-12       2.0   1.0  0.012377  10.000000\n",
      "2127480     873      0  2016-07-13       0.0   1.0       NaN        NaN\n",
      "2127481     873      0  2016-10-14       0.0   0.0       NaN        NaN\n",
      "2127482     873      0  2017-03-19       0.0   1.0       NaN        NaN\n",
      "2127483     873      0  2017-08-31       0.0   1.0       NaN        NaN\n",
      "2127484     873      0  2017-09-06       1.0   1.0  0.012377   7.979798\n",
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   "source": []
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